A generative model learns patterns from adaptive QAOA circuits to generate high-quality shallow quantum circuits for Max-E3-SAT that scale better than variational baselines.
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22 Pith papers cite this work. Polarity classification is still indexing.
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Complementarity constraints are treated as a Lie group under relaxation to enable parameterization that satisfies them by construction in LCQP solvers.
A hybrid controller samples low-dimensional end-effector targets for a contact-free stage then runs local complementarity MPC at each sample to approximate global contact-implicit optimization.
ProF repairs DNNs for individual fairness by using interval bound propagation to bound outputs over input sets and solving a MILP to adjust the model with guarantees on those sets.
DQI-Kit automates encoding of objectives and constraints into Max-LINSAT instances and estimates expected DQI performance on the resulting problems.
A frequency nadir-constrained MILP framework for black-start restoration that incorporates energy storage to maintain frequency security and shorten recovery time.
DM³-Nav delivers decentralized multi-agent semantic navigation for multimodal open-vocabulary multi-object tasks that matches centralized baselines in simulation and succeeds in real-world robot deployments.
Augmented graphs of convex sets encode the TSP specification to solve it exactly as a shortest path problem, with a precise link to the Bellman-Held-Karp algorithm and a scalable branch-and-bound heuristic using minimum 1-trees.
A nested amplitude amplification protocol for knapsack performs partial amplification on initial variables via an Inner Iteration Finder before global GAS, reducing solution improvement costs versus baseline in simulations on large instances.
ODYN is a novel all-shifted non-interior-point primal-dual QP solver with strong warm-start performance for robotics and AI applications.
A new series-parallel decomposition algorithm for general DAGs enables task mapping in heterogeneous systems that improves makespan over HEFT variants while running orders of magnitude faster than genetic algorithms or ILPs.
An attention-based DRL agent with Transformer encoder and GNN learns heuristics for qubit-to-core allocation in multi-core quantum systems to minimize state transfers and online compilation time.
SHIELD derives safe certificates from Lagrangian duality to reduce decision variables and constraints in convex programs, accelerated by a transformer network, delivering order-of-magnitude speedups in stochastic MPC for multi-modal traffic with preserved feasibility and safety.
A novel MILP relaxation for the load-dependent TSP yields optimal routes for most instances with up to 50 targets in under one minute, with optimality gaps below 13% for the rest.
An ILP model and BCD heuristic jointly optimize model splitting, node placement, and smashed-data routing in an SFC-based multi-hop split learning/inference architecture to minimize end-to-end latency.
Feasibility-aware imitation learning accelerates Benders decomposition by predicting feasible integer assignments in the master problem, improving solution times over prior imitation learning methods while retaining finite convergence.
LACE-S uses a neural representation with a projection layer and Jacobian regularization to produce locational carbon emission metrics that remain consistent with total emissions and sensitivities, leading to reliable system-wide emission reductions in load-shifting tests unlike prior metrics.
A progressive integrality outer-inner approximation framework solves large-scale AC unit commitment problems faster and more robustly than commercial solvers on 200- and 500-bus networks.
A scalable PTDF-based two-stage optimization framework with trust-region multicut Benders decomposition for co-optimizing transmission and storage under high renewables, demonstrated on a 2000-bus synthetic Texas system with sub-2% optimality gaps.
STALC presents a graph-based hierarchical planner with mixed-integer programming for multi-robot coordination in reconnaissance scenarios, demonstrated in simulation and hardware.
Introduces a decision-aware neural ODE model that integrates outage dynamics prediction with global optimization of resilience interventions for power grids.
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Q3SAT-GPT: A Generative Model for Discovering Quantum Circuits for the 3-SAT Problem
A generative model learns patterns from adaptive QAOA circuits to generate high-quality shallow quantum circuits for Max-E3-SAT that scale better than variational baselines.
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Complementarity by Construction: A Lie-Group Approach to Solving Quadratic Programs with Linear Complementarity Constraints
Complementarity constraints are treated as a Lie group under relaxation to enable parameterization that satisfies them by construction in LCQP solvers.
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Approximating Global Contact-Implicit MPC via Sampling and Local Complementarity
A hybrid controller samples low-dimensional end-effector targets for a contact-free stage then runs local complementarity MPC at each sample to approximate global contact-implicit optimization.
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Provable Fairness Repair for Deep Neural Networks
ProF repairs DNNs for individual fairness by using interval bound propagation to bound outputs over input sets and solving a MILP to adjust the model with guarantees on those sets.
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From Constraint to Code: DQI-Kit -- A Software Framework for Decoded Quantum Interferometry
DQI-Kit automates encoding of objectives and constraints into Max-LINSAT instances and estimates expected DQI performance on the resulting problems.
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Frequency Nadir-Constrained Power System Restoration Planning with Energy Storage
A frequency nadir-constrained MILP framework for black-start restoration that incorporates energy storage to maintain frequency security and shorten recovery time.
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DM$^3$-Nav: Decentralized Multi-Agent Multimodal Multi-Object Semantic Navigation
DM³-Nav delivers decentralized multi-agent semantic navigation for multimodal open-vocabulary multi-object tasks that matches centralized baselines in simulation and succeeds in real-world robot deployments.
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Augmented Graphs of Convex Sets and the Traveling Salesman Problem
Augmented graphs of convex sets encode the TSP specification to solve it exactly as a shortest path problem, with a precise link to the Bellman-Held-Karp algorithm and a scalable branch-and-bound heuristic using minimum 1-trees.
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A Nested Amplitude Amplification Protocol for the Binary Knapsack Problem
A nested amplitude amplification protocol for knapsack performs partial amplification on initial variables via an Inner Iteration Finder before global GAS, reducing solution improvement costs versus baseline in simulations on large instances.
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ODYN: An All-Shifted Non-Interior-Point Method for Quadratic Programming in Robotics and AI
ODYN is a novel all-shifted non-interior-point primal-dual QP solver with strong warm-start performance for robotics and AI applications.
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Static task mapping for heterogeneous systems based on series-parallel decompositions
A new series-parallel decomposition algorithm for general DAGs enables task mapping in heterogeneous systems that improves makespan over HEFT variants while running orders of magnitude faster than genetic algorithms or ILPs.
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Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures
An attention-based DRL agent with Transformer encoder and GNN learns heuristics for qubit-to-core allocation in multi-core quantum systems to minimize state transfers and online compilation time.
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SHIELD: Scalable Optimal Control with Certification using Duality and Convexity
SHIELD derives safe certificates from Lagrangian duality to reduce decision variables and constraints in convex programs, accelerated by a transformer network, delivering order-of-magnitude speedups in stochastic MPC for multi-modal traffic with preserved feasibility and safety.
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An Exact Algorithm for Load-Dependent Traveling Salesman Problem for Unmanned Aerial Vehicle Package Delivery
A novel MILP relaxation for the load-dependent TSP yields optimal routes for most instances with up to 50 targets in under one minute, with optimality gaps below 13% for the rest.
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Optimization of Model Splitting, Placement, and Chaining for Multi-hop Split Learning and Inference
An ILP model and BCD heuristic jointly optimize model splitting, node placement, and smashed-data routing in an SFC-based multi-hop split learning/inference architecture to minimize end-to-end latency.
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Feasibility-Aware Imitation Learning for Benders Decomposition
Feasibility-aware imitation learning accelerates Benders decomposition by predicting feasible integer assignments in the master problem, improving solution times over prior imitation learning methods while retaining finite convergence.
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LACE-S: Toward Sensitivity-consistent Locational Average Carbon Emissions via Neural Representation
LACE-S uses a neural representation with a projection layer and Jacobian regularization to produce locational carbon emission metrics that remain consistent with total emissions and sensitivities, leading to reliable system-wide emission reductions in load-shifting tests unlike prior metrics.
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Warm-Startable Progressive Integrality Outer-Inner Approximation for AC Unit Commitment with Conic Formulation
A progressive integrality outer-inner approximation framework solves large-scale AC unit commitment problems faster and more robustly than commercial solvers on 200- and 500-bus networks.
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High-Resolution PTDF-Based Planning of Storage and Transmission Under High Renewables
A scalable PTDF-based two-stage optimization framework with trust-region multicut Benders decomposition for co-optimizing transmission and storage under high renewables, demonstrated on a 2000-bus synthetic Texas system with sub-2% optimality gaps.
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Stratified Topological Autonomy for Long-Range Coordination (STALC)
STALC presents a graph-based hierarchical planner with mixed-integer programming for multi-robot coordination in reconnaissance scenarios, demonstrated in simulation and hardware.
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Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management
Introduces a decision-aware neural ODE model that integrates outage dynamics prediction with global optimization of resilience interventions for power grids.
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